Overview

Dataset statistics

Number of variables10
Number of observations385500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory29.4 MiB
Average record size in memory80.0 B

Variable types

Numeric10

Alerts

Unnamed: 0 is highly overall correlated with area[1] and 3 other fieldsHigh correlation
area[1] is highly overall correlated with Unnamed: 0 and 4 other fieldsHigh correlation
negpmax[1] is highly overall correlated with Unnamed: 0 and 3 other fieldsHigh correlation
pmax[1] is highly overall correlated with Unnamed: 0 and 4 other fieldsHigh correlation
pmax[2] is highly overall correlated with area[1] and 2 other fieldsHigh correlation
x is highly overall correlated with Unnamed: 0 and 3 other fieldsHigh correlation
y is highly overall correlated with pmax[2]High correlation
negpmax[1] is highly skewed (γ1 = -226.4792477)Skewed
negpmax[2] is highly skewed (γ1 = 416.1662618)Skewed
Unnamed: 0 is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique

Reproduction

Analysis started2024-01-24 23:03:11.750938
Analysis finished2024-01-24 23:03:29.806808
Duration18.06 seconds
Software versionydata-profiling vv4.6.3
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct385500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean192749.5
Minimum0
Maximum385499
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2024-01-25T00:03:29.932560image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19274.95
Q196374.75
median192749.5
Q3289124.25
95-th percentile366224.05
Maximum385499
Range385499
Interquartile range (IQR)192749.5

Descriptive statistics

Standard deviation111284.41
Coefficient of variation (CV)0.57735252
Kurtosis-1.2
Mean192749.5
Median Absolute Deviation (MAD)96375
Skewness-1.3646122 × 10-15
Sum7.4304932 × 1010
Variance1.238422 × 1010
MonotonicityStrictly increasing
2024-01-25T00:03:30.042923image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
256997 1
 
< 0.1%
257006 1
 
< 0.1%
257005 1
 
< 0.1%
257004 1
 
< 0.1%
257003 1
 
< 0.1%
257002 1
 
< 0.1%
257001 1
 
< 0.1%
257000 1
 
< 0.1%
256999 1
 
< 0.1%
Other values (385490) 385490
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
385499 1
< 0.1%
385498 1
< 0.1%
385497 1
< 0.1%
385496 1
< 0.1%
385495 1
< 0.1%
385494 1
< 0.1%
385493 1
< 0.1%
385492 1
< 0.1%
385491 1
< 0.1%
385490 1
< 0.1%

x
Real number (ℝ)

HIGH CORRELATION 

Distinct81
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean397.72374
Minimum200
Maximum600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2024-01-25T00:03:30.155984image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum200
5-th percentile215
Q1285
median400
Q3500
95-th percentile585
Maximum600
Range400
Interquartile range (IQR)215

Descriptive statistics

Standard deviation120.5931
Coefficient of variation (CV)0.30320822
Kurtosis-1.197003
Mean397.72374
Median Absolute Deviation (MAD)110
Skewness0.034705739
Sum1.533225 × 108
Variance14542.697
MonotonicityIncreasing
2024-01-25T00:03:30.273023image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
235 6600
 
1.7%
580 6300
 
1.6%
565 6300
 
1.6%
410 6100
 
1.6%
405 6100
 
1.6%
200 5900
 
1.5%
590 5900
 
1.5%
225 5900
 
1.5%
355 5800
 
1.5%
585 5800
 
1.5%
Other values (71) 324800
84.3%
ValueCountFrequency (%)
200 5900
1.5%
205 5300
1.4%
210 5600
1.5%
215 5600
1.5%
220 4800
1.2%
225 5900
1.5%
230 5600
1.5%
235 6600
1.7%
240 5800
1.5%
245 5800
1.5%
ValueCountFrequency (%)
600 5300
1.4%
595 5500
1.4%
590 5900
1.5%
585 5800
1.5%
580 6300
1.6%
575 5100
1.3%
570 5500
1.4%
565 6300
1.6%
560 5500
1.4%
555 5800
1.5%

y
Real number (ℝ)

HIGH CORRELATION 

Distinct81
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean404.10636
Minimum200
Maximum600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2024-01-25T00:03:30.386334image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum200
5-th percentile225
Q1310
median405
Q3500
95-th percentile580
Maximum600
Range400
Interquartile range (IQR)190

Descriptive statistics

Standard deviation113.65223
Coefficient of variation (CV)0.28124336
Kurtosis-1.1513592
Mean404.10636
Median Absolute Deviation (MAD)95
Skewness-0.027233727
Sum1.55783 × 108
Variance12916.829
MonotonicityNot monotonic
2024-01-25T00:03:30.512674image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
390 6100
 
1.6%
485 5900
 
1.5%
495 5900
 
1.5%
540 5700
 
1.5%
400 5600
 
1.5%
395 5600
 
1.5%
380 5500
 
1.4%
375 5500
 
1.4%
525 5400
 
1.4%
520 5400
 
1.4%
Other values (71) 328900
85.3%
ValueCountFrequency (%)
200 3800
1.0%
205 3400
0.9%
210 3900
1.0%
215 3400
0.9%
220 3900
1.0%
225 4100
1.1%
230 4600
1.2%
235 4800
1.2%
240 4200
1.1%
245 4000
1.0%
ValueCountFrequency (%)
600 4700
1.2%
595 5300
1.4%
590 4700
1.2%
585 4400
1.1%
580 4700
1.2%
575 4500
1.2%
570 4000
1.0%
565 4300
1.1%
560 3900
1.0%
555 4000
1.0%

pmax[1]
Real number (ℝ)

HIGH CORRELATION 

Distinct379190
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.510543
Minimum2.0281342
Maximum106.64907
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2024-01-25T00:03:30.628004image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum2.0281342
5-th percentile4.0803214
Q15.6197278
median10.068513
Q319.031319
95-th percentile55.782428
Maximum106.64907
Range104.62093
Interquartile range (IQR)13.411591

Descriptive statistics

Standard deviation16.781018
Coefficient of variation (CV)1.0163819
Kurtosis4.1340594
Mean16.510543
Median Absolute Deviation (MAD)5.0438627
Skewness2.0737665
Sum6364814.4
Variance281.60257
MonotonicityNot monotonic
2024-01-25T00:03:30.742400image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.899423218 4
 
< 0.1%
4.990899658 4
 
< 0.1%
3.783041382 4
 
< 0.1%
4.045281982 4
 
< 0.1%
4.568475342 4
 
< 0.1%
8.171624756 3
 
< 0.1%
10.64902344 3
 
< 0.1%
4.581112671 3
 
< 0.1%
4.249822998 3
 
< 0.1%
3.928997803 3
 
< 0.1%
Other values (379180) 385465
> 99.9%
ValueCountFrequency (%)
2.028134155 1
< 0.1%
2.032293701 1
< 0.1%
2.099886651 1
< 0.1%
2.108456421 1
< 0.1%
2.112106323 1
< 0.1%
2.132574463 1
< 0.1%
2.158925748 1
< 0.1%
2.167562866 1
< 0.1%
2.169259644 1
< 0.1%
2.201733398 1
< 0.1%
ValueCountFrequency (%)
106.6490662 1
< 0.1%
106.118045 1
< 0.1%
105.2656555 1
< 0.1%
105.0501282 1
< 0.1%
105.0406494 1
< 0.1%
105.0319489 1
< 0.1%
104.9633575 1
< 0.1%
104.8663849 1
< 0.1%
104.8569489 1
< 0.1%
104.5620422 1
< 0.1%

negpmax[1]
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct364979
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-9.1879171
Minimum-9930.1146
Maximum-1.0545077
Zeros0
Zeros (%)0.0%
Negative385500
Negative (%)100.0%
Memory size2.9 MiB
2024-01-25T00:03:30.847568image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum-9930.1146
5-th percentile-30.781083
Q1-8.1143234
median-5.2290091
Q3-4.3876862
95-th percentile-3.5128902
Maximum-1.0545077
Range9929.0601
Interquartile range (IQR)3.7266373

Descriptive statistics

Standard deviation29.95529
Coefficient of variation (CV)-3.2602917
Kurtosis62600.461
Mean-9.1879171
Median Absolute Deviation (MAD)1.1107627
Skewness-226.47925
Sum-3541942
Variance897.31938
MonotonicityNot monotonic
2024-01-25T00:03:30.955000image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-4.984194946 5
 
< 0.1%
-4.259552002 5
 
< 0.1%
-4.06647644 5
 
< 0.1%
-4.258639526 5
 
< 0.1%
-4.629296875 5
 
< 0.1%
-4.97638855 4
 
< 0.1%
-3.758108521 4
 
< 0.1%
-4.22713623 4
 
< 0.1%
-4.852233887 4
 
< 0.1%
-4.606228638 4
 
< 0.1%
Other values (364969) 385455
> 99.9%
ValueCountFrequency (%)
-9930.114637 1
< 0.1%
-8914.510761 1
< 0.1%
-6181.483775 1
< 0.1%
-5590.745816 1
< 0.1%
-4685.016288 1
< 0.1%
-3934.731699 1
< 0.1%
-3853.931695 1
< 0.1%
-3121.732245 1
< 0.1%
-1236.795412 1
< 0.1%
-1235.522496 1
< 0.1%
ValueCountFrequency (%)
-1.054507737 1
< 0.1%
-1.092450323 1
< 0.1%
-1.114030558 1
< 0.1%
-1.266039295 1
< 0.1%
-1.434287936 1
< 0.1%
-1.446317438 1
< 0.1%
-1.478857042 1
< 0.1%
-1.52775563 1
< 0.1%
-1.568168731 1
< 0.1%
-1.571007391 1
< 0.1%

area[1]
Real number (ℝ)

HIGH CORRELATION 

Distinct384411
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.504198
Minimum-0.99730103
Maximum152.5177
Zeros0
Zeros (%)0.0%
Negative56
Negative (%)< 0.1%
Memory size2.9 MiB
2024-01-25T00:03:31.057163image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum-0.99730103
5-th percentile2.2930189
Q14.7558765
median7.8865518
Q312.945553
95-th percentile29.272564
Maximum152.5177
Range153.515
Interquartile range (IQR)8.1896768

Descriptive statistics

Standard deviation8.4441053
Coefficient of variation (CV)0.80387912
Kurtosis3.7551504
Mean10.504198
Median Absolute Deviation (MAD)3.6987946
Skewness1.758514
Sum4049368.2
Variance71.302914
MonotonicityNot monotonic
2024-01-25T00:03:31.172534image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.760314941 3
 
< 0.1%
7.796130371 3
 
< 0.1%
8.701928711 3
 
< 0.1%
7.823051147 2
 
< 0.1%
3.728522339 2
 
< 0.1%
4.612001953 2
 
< 0.1%
9.907689209 2
 
< 0.1%
13.49553223 2
 
< 0.1%
2.576600342 2
 
< 0.1%
11.59768799 2
 
< 0.1%
Other values (384401) 385477
> 99.9%
ValueCountFrequency (%)
-0.9973010254 1
< 0.1%
-0.5701617432 1
< 0.1%
-0.566237793 1
< 0.1%
-0.5136602783 1
< 0.1%
-0.4972058105 1
< 0.1%
-0.3942199707 1
< 0.1%
-0.3917431641 1
< 0.1%
-0.3896899414 1
< 0.1%
-0.3690429687 1
< 0.1%
-0.3589758301 1
< 0.1%
ValueCountFrequency (%)
152.5176996 1
< 0.1%
145.6036987 1
< 0.1%
122.160965 1
< 0.1%
121.4542615 1
< 0.1%
117.0120544 1
< 0.1%
107.976897 1
< 0.1%
101.1730835 1
< 0.1%
96.30658569 1
< 0.1%
86.8817926 1
< 0.1%
86.23358765 1
< 0.1%

tmax[1]
Real number (ℝ)

Distinct76733
Distinct (%)19.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.383326
Minimum0
Maximum204.6
Zeros147
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2024-01-25T00:03:31.281381image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile40.69829
Q171.2
median71.8
Q372.4
95-th percentile156
Maximum204.6
Range204.6
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation30.102913
Coefficient of variation (CV)0.38901032
Kurtosis6.027981
Mean77.383326
Median Absolute Deviation (MAD)0.6
Skewness2.0292615
Sum29831272
Variance906.18536
MonotonicityNot monotonic
2024-01-25T00:03:31.388311image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71.4 25658
 
6.7%
71.8 25642
 
6.7%
72.2 25528
 
6.6%
71.2 24545
 
6.4%
71.6 24224
 
6.3%
72 24175
 
6.3%
71 24095
 
6.3%
72.4 23797
 
6.2%
70.8 18355
 
4.8%
72.6 16461
 
4.3%
Other values (76723) 153020
39.7%
ValueCountFrequency (%)
0 147
< 0.1%
0.4 126
< 0.1%
0.6 135
< 0.1%
0.8 138
< 0.1%
1 141
< 0.1%
1.137011271 1
 
< 0.1%
1.177317574 1
 
< 0.1%
1.191759708 1
 
< 0.1%
1.2 152
< 0.1%
1.216407622 1
 
< 0.1%
ValueCountFrequency (%)
204.6 188
< 0.1%
204.4 63
 
< 0.1%
204.2 44
 
< 0.1%
204 31
 
< 0.1%
203.8 29
 
< 0.1%
203.6 34
 
< 0.1%
203.4 18
 
< 0.1%
203.2 21
 
< 0.1%
203 29
 
< 0.1%
202.9065489 1
 
< 0.1%

rms[1]
Real number (ℝ)

Distinct385499
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3356601
Minimum0.3147115
Maximum5.8606258
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2024-01-25T00:03:31.492759image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0.3147115
5-th percentile0.81878921
Q11.0926758
median1.3118977
Q31.5525951
95-th percentile1.9347585
Maximum5.8606258
Range5.5459143
Interquartile range (IQR)0.45991925

Descriptive statistics

Standard deviation0.34072918
Coefficient of variation (CV)0.25510172
Kurtosis0.58938045
Mean1.3356601
Median Absolute Deviation (MAD)0.22911796
Skewness0.44698302
Sum514896.95
Variance0.11609638
MonotonicityNot monotonic
2024-01-25T00:03:31.690985image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.169328167 2
 
< 0.1%
1.5639021 1
 
< 0.1%
1.284208478 1
 
< 0.1%
1.026357219 1
 
< 0.1%
1.964471024 1
 
< 0.1%
0.9850057355 1
 
< 0.1%
0.9868609863 1
 
< 0.1%
1.261303057 1
 
< 0.1%
0.5861492999 1
 
< 0.1%
2.300488721 1
 
< 0.1%
Other values (385489) 385489
> 99.9%
ValueCountFrequency (%)
0.3147114992 1
< 0.1%
0.3182394474 1
< 0.1%
0.3200119208 1
< 0.1%
0.3313449944 1
< 0.1%
0.3362450187 1
< 0.1%
0.341345692 1
< 0.1%
0.3484846158 1
< 0.1%
0.3493785362 1
< 0.1%
0.35160784 1
< 0.1%
0.3523950381 1
< 0.1%
ValueCountFrequency (%)
5.860625791 1
< 0.1%
5.609123479 1
< 0.1%
5.444333712 1
< 0.1%
5.333377557 1
< 0.1%
5.274276327 1
< 0.1%
4.873780041 1
< 0.1%
4.826908852 1
< 0.1%
4.639713733 1
< 0.1%
4.536820707 1
< 0.1%
4.382918934 1
< 0.1%

pmax[2]
Real number (ℝ)

HIGH CORRELATION 

Distinct369508
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.7840171
Minimum1.7994354
Maximum68.837366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2024-01-25T00:03:31.795060image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum1.7994354
5-th percentile3.7401396
Q14.6812714
median5.7147125
Q39.7964979
95-th percentile23.915157
Maximum68.837366
Range67.03793
Interquartile range (IQR)5.1152265

Descriptive statistics

Standard deviation7.6926922
Coefficient of variation (CV)0.87576016
Kurtosis11.584336
Mean8.7840171
Median Absolute Deviation (MAD)1.4224274
Skewness3.0676773
Sum3386238.6
Variance59.177513
MonotonicityNot monotonic
2024-01-25T00:03:31.903915image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.836102295 5
 
< 0.1%
4.34810791 5
 
< 0.1%
5.032873535 4
 
< 0.1%
4.290194702 4
 
< 0.1%
5.365795898 4
 
< 0.1%
4.911050415 4
 
< 0.1%
4.643096924 4
 
< 0.1%
4.17623291 4
 
< 0.1%
5.4300354 4
 
< 0.1%
4.778744507 4
 
< 0.1%
Other values (369498) 385458
> 99.9%
ValueCountFrequency (%)
1.799435425 1
< 0.1%
1.853723145 1
< 0.1%
1.985113525 1
< 0.1%
1.986657715 1
< 0.1%
1.994247437 1
< 0.1%
2.006564331 1
< 0.1%
2.052926636 1
< 0.1%
2.059832764 1
< 0.1%
2.065423584 1
< 0.1%
2.097769165 1
< 0.1%
ValueCountFrequency (%)
68.83736572 1
< 0.1%
68.67091675 1
< 0.1%
68.42910767 1
< 0.1%
68.31914063 1
< 0.1%
68.09422302 1
< 0.1%
66.91794739 1
< 0.1%
66.7976532 1
< 0.1%
66.77619019 1
< 0.1%
66.77025146 1
< 0.1%
66.71678467 1
< 0.1%

negpmax[2]
Real number (ℝ)

SKEWED 

Distinct355150
Distinct (%)92.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.5967766
Minimum-10777.684
Maximum74827.377
Zeros0
Zeros (%)0.0%
Negative385498
Negative (%)> 99.9%
Memory size2.9 MiB
2024-01-25T00:03:32.010802image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum-10777.684
5-th percentile-13.628266
Q1-5.67435
median-4.873082
Q3-4.2457882
95-th percentile-3.4598232
Maximum74827.377
Range85605.061
Interquartile range (IQR)1.4285618

Descriptive statistics

Standard deviation173.28984
Coefficient of variation (CV)-30.962437
Kurtosis180470.74
Mean-5.5967766
Median Absolute Deviation (MAD)0.69522508
Skewness416.16626
Sum-2157557.4
Variance30029.37
MonotonicityNot monotonic
2024-01-25T00:03:32.108577image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-4.656845093 6
 
< 0.1%
-4.621166992 6
 
< 0.1%
-5.173995972 5
 
< 0.1%
-4.231277466 5
 
< 0.1%
-4.821792603 5
 
< 0.1%
-4.762768555 5
 
< 0.1%
-4.652124023 5
 
< 0.1%
-4.58092041 5
 
< 0.1%
-5.236935425 5
 
< 0.1%
-4.399459839 5
 
< 0.1%
Other values (355140) 385448
> 99.9%
ValueCountFrequency (%)
-10777.68426 1
< 0.1%
-9654.936352 1
< 0.1%
-7886.759888 1
< 0.1%
-6961.193798 1
< 0.1%
-4693.691574 1
< 0.1%
-3893.580649 1
< 0.1%
-2457.431226 1
< 0.1%
-1710.107671 1
< 0.1%
-1254.480358 1
< 0.1%
-1048.011837 1
< 0.1%
ValueCountFrequency (%)
74827.37723 1
< 0.1%
74820.43968 1
< 0.1%
-0.5405925584 1
< 0.1%
-1.073634097 1
< 0.1%
-1.122057142 1
< 0.1%
-1.206915824 1
< 0.1%
-1.220637375 1
< 0.1%
-1.336545495 1
< 0.1%
-1.36688917 1
< 0.1%
-1.367618803 1
< 0.1%

Interactions

2024-01-25T00:03:27.805432image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:17.366975image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:18.586920image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:19.774197image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:21.040612image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:22.186145image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:23.283123image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:24.407550image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:25.585271image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:26.684819image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:27.918311image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:17.505317image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:18.710860image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:19.898455image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:21.159835image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:22.306263image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:23.404294image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:24.518509image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:25.706159image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:26.805771image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:28.038679image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:17.630045image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:18.829710image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:20.018223image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:21.278096image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:22.423617image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:23.518283image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:24.628812image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:25.822097image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:26.919868image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:28.160738image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:17.759769image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:18.957210image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:20.141232image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:21.397160image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:22.535570image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:23.635290image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:24.745166image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:25.937557image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:27.034622image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:28.281684image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:17.877209image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:19.074103image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:20.252109image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:21.509918image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:22.647053image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:23.746880image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:24.943189image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:26.044036image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:27.141628image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:28.387173image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:17.994990image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:19.188734image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:20.368140image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:21.622323image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:22.754118image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:23.852882image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:25.050140image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:26.150680image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:27.250319image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:28.504117image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:18.120742image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:19.314520image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:20.491874image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:21.744881image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:22.863035image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:23.971451image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:25.162034image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:26.263214image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:27.364300image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:28.609699image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:18.235963image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:19.430070image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:20.605275image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:21.856235image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:22.966646image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:24.078885image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:25.264200image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:26.366414image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:27.471310image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:28.719864image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:18.352509image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:19.544958image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:20.805191image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:21.965942image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:23.072016image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:24.188232image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:25.374682image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:26.471060image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:27.578622image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:28.834452image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:18.475131image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:19.663314image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:20.925382image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:22.083112image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:23.180374image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:24.302740image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:25.483243image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:26.581969image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:27.694059image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Correlations

2024-01-25T00:03:32.182307image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Unnamed: 0area[1]negpmax[1]negpmax[2]pmax[1]pmax[2]rms[1]tmax[1]xy
Unnamed: 01.0000.764-0.617-0.2340.8680.466-0.003-0.1931.0000.017
area[1]0.7641.000-0.508-0.2600.8920.577-0.006-0.1620.7610.253
negpmax[1]-0.617-0.5081.0000.315-0.623-0.486-0.0050.161-0.614-0.237
negpmax[2]-0.234-0.2600.3151.000-0.276-0.1480.0050.064-0.231-0.236
pmax[1]0.8680.892-0.623-0.2761.0000.648-0.003-0.1800.8640.291
pmax[2]0.4660.577-0.486-0.1480.6481.000-0.002-0.1070.4590.574
rms[1]-0.003-0.006-0.0050.005-0.003-0.0021.000-0.018-0.003-0.001
tmax[1]-0.193-0.1620.1610.064-0.180-0.107-0.0181.000-0.193-0.050
x1.0000.761-0.614-0.2310.8640.459-0.003-0.1931.0000.003
y0.0170.253-0.237-0.2360.2910.574-0.001-0.0500.0031.000

Missing values

2024-01-25T00:03:28.932109image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-25T00:03:29.165066image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0xypmax[1]negpmax[1]area[1]tmax[1]rms[1]pmax[2]negpmax[2]
00200.0200.05.409161-17.7212104.53877822.6000001.5639026.084506-19.892543
11200.0200.04.414289-4.7368273.720435111.8750581.2280584.507257-3.787175
22200.0200.04.567191-5.9854373.490490107.8000001.0154085.384155-3.948853
33200.0200.05.019058-4.2299496.786200175.6000001.3506204.165598-5.576041
44200.0200.03.250262-5.7835872.449456159.8000000.7595154.736023-5.137939
55200.0200.03.724728-3.4055532.957784199.6000001.4490234.122510-5.204761
66200.0200.04.516006-4.6080783.4670536.6000001.6825535.444962-4.964096
77200.0200.03.498582-6.1202514.314651152.0924872.0709354.786600-4.250989
88200.0200.05.121312-3.36078410.358022196.0195530.7296154.624289-5.028360
99200.0200.02.741562-8.0193310.91672124.0000001.3521374.208252-5.341691
Unnamed: 0xypmax[1]negpmax[1]area[1]tmax[1]rms[1]pmax[2]negpmax[2]
385490385490600.0595.046.422449-26.96970223.42578672.21.22821556.954974-32.726605
385491385491600.0595.047.052533-25.45619524.63608371.61.49463460.850900-34.080374
385492385492600.0595.055.248883-30.26979426.27130471.21.26171568.094223-33.020096
385493385493600.0595.058.318015-26.36741030.41185971.01.58985768.429108-34.468567
385494385494600.0595.055.444376-30.90285325.10959872.21.30611968.670917-34.107495
385495385495600.0595.049.819376-26.26662324.78140371.01.02145055.651440-29.947864
385496385496600.0595.051.016495-26.13847427.09089471.41.57093958.084024-34.662131
385497385497600.0595.046.661682-30.94311524.98034771.21.25583261.840103-35.670273
385498385498600.0595.046.515765-22.52036733.16307371.41.46544349.772070-33.298181
385499385499600.0595.053.988147-26.61689531.29471771.61.20762259.774219-31.891431